The present disclosure generally relates to the field of feedback controllers, and more particularly to detecting a control loop interaction between two or more control loops.
Feedback controllers are used to control variable devices such as valves, pumps, and dampers in control systems or processes. The object of such controllers is to control the device in a way that maintains a controlled variable (e.g., temperature, humidity, flow rate, pressure, etc.) at a desired setpoint. Many feedback controllers respond to feedback based on one or more control parameters. A common control parameter used in feedback algorithms is proportional gain (i.e., the proportional term, the gain, etc.)—a value that is used by a feedback algorithm to determine the magnitude of the adjustment to the controlled signal given the error signal. For example, when provided the same error signal, a feedback algorithm with a high gain generally results in a large adjustment to the controlled signal while a small gain generally results in a small adjustment to the controlled signal. In addition to the proportional gain an integral term is often used by feedback algorithms (e.g., in proportional plus integral (PI) control algorithms, in proportional-integral-derivative (PID) control algorithms, etc.).
In dynamic systems (e.g., where conditions outside of the control loop are affecting the controlled variable or where an aspect of the control loop is variably imperfect), the optimal control parameters for the feedback algorithm are often also dynamic. Accordingly, some feedback controllers or feedback algorithms are periodically tuned (e.g., manually, automatically) based on observed historical behavior of the system. Other feedback controllers or feedback algorithms include adaptive tuning algorithms that automatically adjust the control parameters during normal operation of the feedback algorithm. Such adaptive tuning algorithms can provide for improved performance relative to tuning algorithms that run only periodically.
Pattern recognition adaptive control (PRAC) defines one class of adaptive tuning controllers. With PRAC, parameters that characterize the pattern of the closed-loop response are determined after significant setpoint changes or load disturbances have occurred. The control parameters for the feedback controller are then adjusted based on the determined pattern characteristics.